A SOCIAL MEDIA STUDY ON THE EFFECTS OF PSYCHIATRIC MEDICATION USE - - PowerPoint PPT Presentation

A SOCIAL MEDIA STUDY ON THE EFFECTS OF PSYCHIATRIC MEDICATION USE

Koustuv Saha, Benjamin Sugar, John Torous, Bruno Abrahao, Emre Kıcıman, Munmun De Choudhury

Saha, K., Sugar, B., Torous, J., Abrahao, B., Kıcıman, E., & De Choudhury, M. (2019, July). A Social Media Study on the Effects of Psychiatric Medication Use. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 13, No. 01, pp. 440-451)., https://wvvw.aaai.org/ojs/index.php/ICWSM/article/view/3242

• One in Six Americans take psychiatric medications
• Five of the top 50 drugs sold in the U.S. are psychiatric medications

1

EFFECTS OF PSYCHIATRIC MEDICATIONS

• Their mechanism of action poorly understood
• Selection of drug treatments is primarily on trial-and-error basis
• Understanding the effects of psychiatric medication is important
• Clinical trials and reports of adverse events
• Limitations with the existing methodologies

2

OUR WORK

...conducts a large-scale social media study of the effects

• f 49 FDA approved antidepressants across four major

families (SSRIs, SNRIs, TCAs, and TeCAs) …adopts a patient-centered approach to study the effects

• f these drugs as reflected and self-reported in

longitudinal social media data

3

PATIENT-CENTERED APPROACH

v Historically, psychiatric care has adopted a “Disease-Centered Model”

§ Neglects the psychoactive effects of drugs

v Consequently, a “Drug-Centered Model” has been advocated

§ Care becomes more collaborative

v “Patient-Centered Model”

4

• Psy. Drug mentions in

2015-16 Personal Drug Intake Classifier Stream Data Account created before 2015? LIWC n-grams Depression Anxiety Stress Psychosis Suicide Control User Data Treatment User Data Treatment Covariates Control Covariates Compute covariates on data before treatment dates

• P. Affect
• N. Affect

Cognition Depression Anxiety Psychosis Suicide Stratify similar users on propensity scores Compare the outcomes of similar users Compile Treatment & Control Data ….. Covariates Outcomes User strata Self-reports of Intake Twitter Data Relative Treatment Effect (RTE) per drug Construct Before and After samples data

Treatment Date Control Date

5

DATA

6

List of psychiatric medications 49 medications Medication Self- intake classifier (Klein et al. 2017) 93,275 self- intake posts

I’m taking my first dose of X tonight. First day on X. Dose 1 taken, and I already feel weird from it. My no-med experiment went horribly awry, so I’m starting X today.

Twitter posts mentioning psychiatric medications 601,134 posts by 230,573 unique users

7

Sertraline Escitalopram Fluoxetine Duloxetine Citalopram Venlafaxine Mirtazapine Paroxetine Amitriptyline Bupropion Buspirone Atomoxetine Desvenlafaxine Doxepin Dosulepin Fluvoxamine Imipramine Vortioxetine Clomipramine

1 2 3 4 5 6 7 8 9 101112

Month

1 2 3 4

10 100 1000 10000

Posts

TREATMENT AND CONTROL DATASET

• Twitter timelines of 23,191 users who self-reported psychiatric

medication intake (2015-2016) [Treatment]

• Random Twitter user timelines (283,374 users) [Control]

8

STUDY DESIGN

• Conduct Observational Study
• Adopt a Causal Inference Framework based on Matching
• Compare the outcomes of similar (matched) individuals,

those exposed to a treatment (psychiatric drug intake), and those who were not.

• Use stratified propensity score analysis

9

BEFORE AND AFTER SAMPLES

• For Treatment users, the before and after the date of

the first intake of medications

• For Control users, we simulate placebo dates by non-

parametrically assigning dates from the distribution of Treatment dates.

10

MEASURING SYMPTOMATIC OUTCOMES

• Affect and Cognition
• Depression, Anxiety, Stress, Psychosis, and Suicidal

Ideation

11

AFFECT, COGNITION

LIWC categories of

• positive and negative affect for affect,
• cognitive mechanics, causation, certainty, inhibition,

discrepancies, negation, and tentativeness for cognition

12

DEPRESSION, ANXIETY, STRESS, PSYCHOSIS, SUICIDAL IDEATION

• Supervised learning classifiers trained on Reddit

domain-specific datasets

• Positive class from r/depression, r/anxiety, r/stress,

r/psychosis, r/SuicideWatch

• Negative class from 20M Reddit posts gathered from 20

subreddits (eg. r/AskReddit,r/aww, r/movies).

13

MATCHING FOR CAUSAL INFERENCE

Covariates:

• Social Attributes (#tweets, #followers, #followees, duration on

platform, frequency of posting)

• Top 2,000 unigrams
• Normalized psycholinguistic occurrence of LIWC categories
• Baseline mental health status (aggregated use of depression,

anxiety, stress, psychosis, and suicidal ideation)

14

PROPENSITY SCORE ANALYSIS

• Use logistic regression classifier to

estimate propensity scores on covariates

• 100 strata of equal width of propensity

scores

• Standardized differences to measure

balance of covariates

0.0 0.2 0.4 0.6 0.8 1.0

Propensity Score

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Density (Users)

µ +/- 2σ

15

TREATMENT EFFECT

• Relative Treatment Effect (RTE)
• Ratio of change in outcome of Tr. And Ct. users per stratum
• Individual Treatment Effect (ITE)
• Regress on psycholinguistic attributes and outcome in Ct. users
• Predict on attributes of matched Tr. users
• Ratio of actual (counterfactual) and predicted outcome

16

RELATIVE TREATMENT EFFECT

Amitriptyline Atomoxetine Bupropion Buspirone Citalopram Clomipramine Desvenlafaxine Dosulepin Doxepin Duloxetine Escitalopram Fluoxetine Fluvoxamine Imipramine Mirtazapine Nortriptyline Paroxetine Sertraline Venlafaxine Vortioxetine SSNRI SSRI TCA TeCA

• P. Affect
• N. Affect

Cognition Depression Anxiety Psychosis Suicidal Idn.

1.0 1.5 2.5 3.0 2.0 0.5

17

INDIVIDUAL TREATMENT EFFECT

18

• Blues indicate

greater likelihood

• f improvement
• Reds indicate

greater likelihood

• f worsening

DISCUSSION

19

TAKEAWAYS

• Social media is an effective sensor to scalably detect

behavioral changes in those who initiate treatment via (self- reported) use of psychiatric medications

• Observe that people’s online behaviors change in some

unexpected ways following drug intake

20

POLICY AND ETHICS

• Potential negative consequences of this work
• Ethical complexities
• Self-treatment

21

CLINICAL IMPLICATIONS

• Patient-Centered Approach to Pharmacological Care
• Complementary insights into the effects of drugs
• Pre-treatment signals seem to be predictive of individual

drug success

• Drug Repurposing
• Low cost and high volume assessments of people’s own reports

and perceptions related to antidepressants’ use

22

TECHNOLOGICAL IMPLICATIONS

• Technologies for Regulatory Bodies
• Technologies for Drug Safety Surveillance
• Technologies to support Digital Therapeutics

23

A Social Media Study on the Effects of Psychiatric Medication Use

Thank You @kous2v| koustuv.saha@gatech.edu | koustuv.com

Saha, K., Sugar, B., Torous, J., Abrahao, B., Kıcıman, E., & De Choudhury, M. (2019, July). A Social Media Study on the Effects of Psychiatric Medication Use. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 13, No. 01, pp. 440-451)., https://wvvw.aaai.org/ojs/index.php/ICWSM/article/view/3242